Knowledge Management and Data Driven Decision Making

There is probably no segment of activity in the education world attracting as much attention at present as that of knowledge management in terms of data driven decision making. In this article author Mike King will define the content of the five categories we use in knowledge management of data in the pre-development stages of our action plan.

There is probably no segment of activity in the education world attracting as much attention at present as that of knowledge management in terms of data driven decision making. In this article author Mike King will define the content of the five categories we use in knowledge management of data in the pre-development stages of our action plan.

There is probably no segment of activity in the education world attracting as much attention atpresent as that of knowledge management in terms of data driven decision making. In thisportion of our presentation we will define the content of the five categories we use inknowledge management of data in the pre-development stages of our action plan. The firstfour categories relate to the past; they deal with what has been or what is known. The fifthcategory deals with the future because it incorporates present data trends for designingoperational interventions within the system to support current educational needs. In designingoperational interventions educators can create a Zone of Proximal Development rather than just grasp the present and past. But achieving operational interventions isn't easy; our teachersmust move successively through four succinct activities when using data to create a zone of proximal development when designing operational interventions. The five categories we use indeveloping operational interventions include;Data: Raw Observations and Measurements



Information: data that are processed to be useful; provides answers to "who", "what","where", and "when" questions

Understanding: appreciation of "why"The Zone of Proximal Development, or ZPD, was originally established by the Russianpsychologist Lev Vygotsky and refers to the distance between what a child can do withassistance and what the child can accomplish without assistance. The term used within thecontext of Data Management has a similar connotation except that within the data analysissystem it becomes both a precursor to action plan development and is a provision for thecontinuous construction of operational interventions. Operational interventions play a key rolewithin the data loop since it defines the process needed to scaffold data as it is beingmonitored against attainable performance levels established within the action plan.(SeeKnowledge Management and Data Driven Decision Making Flow Chart)

At the first level of creating operational interventions, is data and it comes in the form of rawobservations and measurements. In the past we have used raw data as a source of determiningour rate of success. We also prematurely used the data in its raw form to construct our actionplan by measuring student abilities as they enter and exit our school as well as designingimmediate interventions to gage our future success rates. This process is no longer used since itis in this step that we have recognized that raw data in isolation is not useful in terms of relationships to current practices. This data is only useful as we reach the second level inoperational intervention development; that of information building.

Information: "Who", "What", "Where", and "When"

At the second level of creating operational interventions we begin the process of informationbuilding. At this level data is converted into meaning by way of relational connection. This"meaning" can be useful, but at times becomes distorted through mythical assumptions aboutcurrent practices that may not be substantiated through knowledge. For example when we lookat our current data of students transitioning into our school we may make the generalassumption that the transition itself is the cause for a dip in student performance. We maymake general assumptions known as myths on multiple reasons why this dip is occurring thatrange from teaching practices, school scheduling, to students developmental readiness. It is atthe third level of creating operational interventions that knowledge is applied to the datagathering process. Traditionally it has been at this stage where we have generated SMART

At the third level of creating operational interventions knowledge becomes essential in theappropriate collection of information, such that it's intent is to be useful. Knowledge is adeterministic process. When someone "memorizes" information (as less-aspiring test-boundstudents often do), then they have amassed knowledge. This knowledge has useful meaning tothem, but it does not provide for, in and of itself, an integration such as would infer furtherknowledge. For example our school has set a yearly goal for making AYP. To accomplish thisgoal we measure the schools comprehensive ability to make specific growth as it relates to aspecific grade level. In other words we use grade level data that has no relevance to individualstudent learning. In this current system the relevance on student learning is not on mastery buton the individual schools strategies in making AYP, thus within the system each school becomesits own test preparatory island of AYP obtainment. To correctly establish a SMART Goal orcreate an action plan built on data requires a true cognitive and analytical ability that is onlyencompassed in the fourth level and that is generating an understanding of how data becomesa probabilistic process.

Understanding: Appreciation of "Why"

At the fourth level of creating operational interventions is the process of understanding thedata in terms of relevance to knowledge. In other words what is the data telling us so we cansynthesize and construct new knowledge from the previously held knowledge. For example, aswe review our current level of mastery based on four mastery checks we can gage our currentlevel of progress by making the following statement;"The Mastery Percentage Range chart reflects the curent mastery level for four out of five mastery checks in math and reading for grades seven and eight. Our goal is to haveall students at the mastery level of 80% by the end of the 2011 school year. Toacomplish this goal in reading we will need 14% increase in 8th grade, and and a 4%increase in 7th grade by the time we reach mastery check five retakes. To accomplish an80% mastery goal in math we will need a 26.25% increase in 8th grade and a 13%increase in 7th grade by the time we reach mastery check five retakes."